Prelude - What is Structural Equation Modeling (SEM)? |
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About this Site and How to Use
USGS scientists have been involved for a number of years in the development and use of Structural Equation Modeling (SEM). This methodology represents an approach to statistical modeling that focuses on the study of complex cause-effect hypotheses about the mechanisms operating in systems.More...
What's New?
Currently, these web pages represent the intial public launch of education materials. Updates that follow will be logged here.More...
Feature: Causal networks clarify productivity-richness interrelations, bivariate plots do not
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Photo: Bogong subalpine grassland in Australia, a participating site in the Nutrient Network Global Cooperative. Photo credit: Eric Land, Nutrient Network. |
Lay summary from Functional Ecology:
Species diversity and productivity are among the most fundamental characteristics of ecosystems. While the importance of these ecological properties is universally agreed upon, the mechanisms connecting these two variables have been debated for decades without resolution. In an attempt to achieve a synthetic understanding of the collective effects of proposed mechanisms, some ecologists have turned to the examination of bivariate plots to see if particular patterns are consistently observed in nature.
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Projects
A number of projects involving USGS scientists at the National Wetlands Research Center apply SEM to basic and applied science issues.More...
Publications
- Grace, J.B., Scheiner, S.M., Schoolmaster, D.R. Jr. 2015. Structural equation modeling: building and evaluating causal models. Chapter 8 In: Fox, G.A., Negrete-Yanlelevich, S., and Sosa, V.J. (eds.) Ecological Statistics: Contemporary Theory and Application. Oxford University Press.
- Grace, J.B., Adler, P.B., Harpole, W.S., Borer, E.T., and Seabloom, E.W. 2014 Causal networks clarify productivity–richness interrelations, bivariate plots do not. Functional Ecology, DOI: 10.1111/1365-2435 (early online) (http://onlinelibrary.wiley.com/doi/10.1111/1365-2435.12269/abstract)
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Applications
View a list of applications using SEM.
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I. Introduction and Background |
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SEM Essentials |
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Summary Points (SEM.1.1) |
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Anatomy of SE Models (SEM.1.2) |
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Model Specifications (SEM.1.3) |
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Estimation (SEM.1.4) |
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Path Rules (SEM.1.5) |
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Interpreting Coefficients (SEM.1.6) |
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Categorical Predictors (SEM.1.7) |
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Doing SEM in R |
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Introduction to Lavaan (SEM.2.1) |
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Local Estimation of Equations (SEM.2.2) |
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Model Evaluation (SEM.3) |
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II. Basic Elements of Modeling |
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Overview of the Modeling Process (SEM.4) |
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The Test of Mediation (SEM.5) |
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Test of Mediation Exercise |
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SEM versus Multiple Regression (SEM.6) |
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Causal Modeling Principles Revisited (SEM.7) |
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SEM versus ANOVA and ANCOVA (SEM.8) |
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III. Modeling with Latent and Composite Variables |
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Modeling with Latent Variables (SEM.9) |
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Composites and Formative Indicators (SEM.10.1) |
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Composites and Endogenous Nonlinearities (SEM.10.2) |
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Composites with Multiple Effects (SEM.10.3) |
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Composites - Comparing Specifications (SEM.10.4) |
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IV. Additional Topics |
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Additional lavaan Options |
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Modeling Interactions |
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Spatial Autocorrelation Procedures |
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Spatial Autocorrelation Exercise |
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Reciprocal Effects Overview |
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Adjusting for Nested Data using lavaan.survey |
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